dfch · November 10, 2025 18:47
diff --git a/gistfile1.txt b/gistfile1.txt
 import matplotlib.pyplot as plt
 import numpy as np

 # Create figure
 plt.figure(figsize=(12,8))

 # Axes from 0 to 1 to mimic a quadrant-style chart
 ax = plt.gca()
 ax.set_xlim(0, 1)
 ax.set_ylim(0, 1)

 # Titles and labels
 plt.title("Gartner-ish Quadrant: How Security Maturity Correlates with Outcomes", fontsize=18, weight='bold')
 plt.xlabel("Security Maturity (Policies, Culture, Automation, Zero-Trust)", fontsize=12)
 plt.ylabel("Outcomes: Org Performance, Health & Life Quality", fontsize=12)

 # Draw quadrant lines
 ax.axvline(0.5, color="#B0B7C3", lw=1.5)
 ax.axhline(0.5, color="#B0B7C3", lw=1.5)

 # Background tint by quadrant for subtle emphasis
 ax.fill_between([0,0.5],[0.5,0.5], [1,1], color="#EAF6EF", alpha=0.6)   # Top-left
 ax.fill_between([0.5,1],[0.5,0.5], [1,1], color="#E8F0FE", alpha=0.6)   # Top-right
 ax.fill_between([0,0.5],[0,0], [0.5,0.5], color="#FFF4E5", alpha=0.6)   # Bottom-left
 ax.fill_between([0.5,1],[0,0], [0.5,0.5], color="#FEECEC", alpha=0.6)   # Bottom-right

 # Quadrant labels
 ax.text(0.25, 0.92, "Trying Hard\n(Ad hoc wins)", ha='center', va='top', fontsize=11, color="#2B2F33")
 ax.text(0.75, 0.92, "Aligned & Proactive\n(Compounding benefits)", ha='center', va='top', fontsize=11, color="#2B2F33")
 ax.text(0.25, 0.08, "Clueless & Reactive\n(Incident treadmill)", ha='center', va='bottom', fontsize=11, color="#2B2F33")
 ax.text(0.75, 0.08, "Tool Sprawl w/o Culture\n(Plateaued value)", ha='center', va='bottom', fontsize=11, color="#2B2F33")

 # Synthetic data: monotonic relationship with diminishing returns at extremes
 x = np.linspace(0.05, 0.95, 14)
 np.random.seed(7)
 # Sigmoid-like curve plus small noise
 y = 1/(1+np.exp(-6*(x-0.45)))
 y = 0.1 + 0.8*y + np.random.normal(scale=0.03, size=len(x))
 y = np.clip(y, 0.05, 0.95)

 # Plot points
 plt.scatter(x, y, s=70, color="#D52B1E", edgecolor='white', linewidth=0.8, alpha=0.95, label="Observed initiatives")

 # Trend line
 z = np.polyfit(x, y, 2)
 p = np.poly1d(z)
 xs = np.linspace(0.05, 0.95, 200)
 plt.plot(xs, p(xs), color="#2B2F33", lw=2, label="Trend")

 # Annotations linking security to outcomes
 ax.annotate("Security culture → fewer incidents\n→ lower stress & burnout",
            xy=(0.35, p(0.35)), xytext=(0.15, 0.78),
            arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, color="#2B2F33")
 ax.annotate("Automation & Zero-Trust →\nresilience, uptime, trust",
            xy=(0.6, p(0.6)), xytext=(0.78, 0.68),
            arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, ha='right', color="#2B2F33")
 ax.annotate("Governance + Training →\nconsistent decisions",
            xy=(0.5, p(0.5)), xytext=(0.28, 0.42),
            arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, color="#2B2F33")

 # Axis ticks with friendly labels
 ax.set_xticks([0.1,0.3,0.5,0.7,0.9])
 ax.set_xticklabels(["Ad hoc","Basic","Defined","Managed","Optimized"], rotation=0)
 ax.set_yticks([0.1,0.3,0.5,0.7,0.9])
 ax.set_yticklabels(["Low","Emerging","Good","Strong","Excellent"])

 # Legend and grid
 plt.legend(loc='lower right', frameon=False)
 ax.grid(True, which='both', color="#DEE3EA", linewidth=0.8, alpha=0.7)

 # Subtext footnote
 plt.figtext(0.5, 0.01, "Illustrative 'Gartner-ish' chart: Higher security maturity correlates with better organizational performance, employee health, and life quality (conceptual).", ha='center', fontsize=9, color="#555")

 plt.tight_layout(rect=[0,0.03,1,1])
 # plt.savefig("./chat.png")
 plt.show()
	import matplotlib.pyplot as plt
	import numpy as np

	# Create figure
	plt.figure(figsize=(12,8))

	# Axes from 0 to 1 to mimic a quadrant-style chart
	ax = plt.gca()
	ax.set_xlim(0, 1)
	ax.set_ylim(0, 1)

	# Titles and labels
	plt.title("Gartner-ish Quadrant: How Security Maturity Correlates with Outcomes", fontsize=18, weight='bold')
	plt.xlabel("Security Maturity (Policies, Culture, Automation, Zero-Trust)", fontsize=12)
	plt.ylabel("Outcomes: Org Performance, Health & Life Quality", fontsize=12)

	# Draw quadrant lines
	ax.axvline(0.5, color="#B0B7C3", lw=1.5)
	ax.axhline(0.5, color="#B0B7C3", lw=1.5)

	# Background tint by quadrant for subtle emphasis
	ax.fill_between([0,0.5],[0.5,0.5], [1,1], color="#EAF6EF", alpha=0.6) # Top-left
	ax.fill_between([0.5,1],[0.5,0.5], [1,1], color="#E8F0FE", alpha=0.6) # Top-right
	ax.fill_between([0,0.5],[0,0], [0.5,0.5], color="#FFF4E5", alpha=0.6) # Bottom-left
	ax.fill_between([0.5,1],[0,0], [0.5,0.5], color="#FEECEC", alpha=0.6) # Bottom-right

	# Quadrant labels
	ax.text(0.25, 0.92, "Trying Hard\n(Ad hoc wins)", ha='center', va='top', fontsize=11, color="#2B2F33")
	ax.text(0.75, 0.92, "Aligned & Proactive\n(Compounding benefits)", ha='center', va='top', fontsize=11, color="#2B2F33")
	ax.text(0.25, 0.08, "Clueless & Reactive\n(Incident treadmill)", ha='center', va='bottom', fontsize=11, color="#2B2F33")
	ax.text(0.75, 0.08, "Tool Sprawl w/o Culture\n(Plateaued value)", ha='center', va='bottom', fontsize=11, color="#2B2F33")

	# Synthetic data: monotonic relationship with diminishing returns at extremes
	x = np.linspace(0.05, 0.95, 14)
	np.random.seed(7)
	# Sigmoid-like curve plus small noise
	y = 1/(1+np.exp(-6*(x-0.45)))
	y = 0.1 + 0.8*y + np.random.normal(scale=0.03, size=len(x))
	y = np.clip(y, 0.05, 0.95)

	# Plot points
	plt.scatter(x, y, s=70, color="#D52B1E", edgecolor='white', linewidth=0.8, alpha=0.95, label="Observed initiatives")

	# Trend line
	z = np.polyfit(x, y, 2)
	p = np.poly1d(z)
	xs = np.linspace(0.05, 0.95, 200)
	plt.plot(xs, p(xs), color="#2B2F33", lw=2, label="Trend")

	# Annotations linking security to outcomes
	ax.annotate("Security culture → fewer incidents\n→ lower stress & burnout",
	xy=(0.35, p(0.35)), xytext=(0.15, 0.78),
	arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, color="#2B2F33")
	ax.annotate("Automation & Zero-Trust →\nresilience, uptime, trust",
	xy=(0.6, p(0.6)), xytext=(0.78, 0.68),
	arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, ha='right', color="#2B2F33")
	ax.annotate("Governance + Training →\nconsistent decisions",
	xy=(0.5, p(0.5)), xytext=(0.28, 0.42),
	arrowprops=dict(arrowstyle="->", color="#2B2F33"), fontsize=10, color="#2B2F33")

	# Axis ticks with friendly labels
	ax.set_xticks([0.1,0.3,0.5,0.7,0.9])
	ax.set_xticklabels(["Ad hoc","Basic","Defined","Managed","Optimized"], rotation=0)
	ax.set_yticks([0.1,0.3,0.5,0.7,0.9])
	ax.set_yticklabels(["Low","Emerging","Good","Strong","Excellent"])

	# Legend and grid
	plt.legend(loc='lower right', frameon=False)
	ax.grid(True, which='both', color="#DEE3EA", linewidth=0.8, alpha=0.7)

	# Subtext footnote
	plt.figtext(0.5, 0.01, "Illustrative 'Gartner-ish' chart: Higher security maturity correlates with better organizational performance, employee health, and life quality (conceptual).", ha='center', fontsize=9, color="#555")

	plt.tight_layout(rect=[0,0.03,1,1])
	# plt.savefig("./chat.png")
	plt.show()
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